Work

Exaggeration-based Fake Cybersecurity News Detection

Year: 2025

Type: conference-paper

Abstract: We address the challenge of detecting exaggeration in cybersecurity tweets on X, where misinformation spreads rapidly. Our novel framework uses local Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to gather evidence and assess tweets' rhetorical intensity, offering graded exaggeration scores. Validated through a human study and a pilot that matches LLM results with human labels, this work lays the groundwork for improved misinformation detection tools.

DOI: 10.1145/3733813.3764365

Authors: Abdullah Felemban, Mustafa Ghaleb, Muhamad Felemban

Institutions: The Ohio State University, King Fahd University of Petroleum and Minerals

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

OpenAlex ID: W4414927949

FWCI: 3.581

Cites: 11

Cited by: 2

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